ggplot_murders

library(dslabs)
library(dplyr)

## 
## Adjuntando el paquete: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
data("murders")
ggplot(data = murders)

ggplot(data=murders,aes(x = population/10^6, y = total))+
  geom_point()

murders %>% ggplot(aes(x = population/10^6, y = total)) + 
  geom_point()

murders %>% ggplot() + 
  geom_point(aes(x = population/10^6, y = total))

murders %>% ggplot(aes(x = population/10^6, 
                       y = total,color=region,shape=region)) + 
  geom_point(show.legend = FALSE)+xlab("Populations in millions (log scale)") + 
  ylab("Total number of murders (log scale)") +
  ggtitle("US Gun Murders in 2010")+ 
  scale_x_continuous(trans = "log10") +
  scale_y_continuous(trans = "log10") + 
  facet_wrap(~ region, nrow = 2)

murders %>% ggplot(aes(x = population/10^6, 
                       y = total,color=region,shape=region)) + 
  geom_point(show.legend = FALSE)+xlab("Populations in millions (log scale)") + 
  ylab("Total number of murders (log scale)") +
  ggtitle("US Gun Murders in 2010")+ 
  scale_x_continuous(trans = "log10") +
  scale_y_continuous(trans = "log10") + 
  facet_wrap(~ region, nrow = 2)+ 
  geom_smooth(show.legend = FALSE)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

p<- murders %>% ggplot(aes(x = population/10^6, 
                           y = total,color=region)) + 
  geom_point(aes(shape=region))+xlab("Populations in millions (log scale)") + 
  ylab("Total number of murders (log scale)") +
  ggtitle("US Gun Murders in 2010")+ 
  scale_x_continuous(trans = "log10") +
  scale_y_continuous(trans = "log10") 
p

#install.packages("ggthemes")
library(ggthemes)
p + theme_economist()

#Diagrama de barras

murders %>%
  count(region) %>%
  mutate(porcentaje = (n/sum(n))*100) %>%
  ggplot(aes(region, porcentaje,fill = region)) + 
  geom_bar(stat = "identity") + 
  geom_text(aes(label = sprintf(
   "%.1f%%", porcentaje)),position = position_stack(vjust = 0.8))

#Diagrama de barras ordenado de mayor a menor

murders %>%
  count(region) %>%
  mutate(proportion = n/sum(n)) %>%
  ggplot(aes(x=reorder(region, -n), proportion,fill = region)) + 
  geom_bar(stat = "identity")

#Diagrama de barras ordenado de menor a mayor

murders %>%
  count(region) %>%
  mutate(proportion = n/sum(n)) %>%
  ggplot(aes(x=reorder(region, n), proportion,fill = region)) + 
  geom_bar(stat = "identity")

#Diagrama circular

murders %>%
  count(region) %>%
  mutate(proportion = n/sum(n)) %>%
  ggplot(aes(x="", proportion,fill = region)) + 
  geom_bar(stat = "identity",width = 1) +
  coord_polar(theta = "y") +
  geom_text(aes(label = paste0(
    scales::percent(proportion))),position = position_stack(vjust = 0.5)) 

#Boxplot

murders <- murders %>% mutate(murders,rate= total/population*100000)
murders %>% 
  select(region,rate) %>% 
  filter(region=="South") %>% 
  ggplot(aes(region,rate,fill=region)) + geom_boxplot()

murders %>% ggplot(aes(region,rate,fill=region)) + 
  geom_boxplot()

# Facetas

murders %>%
  count(region) %>%
  mutate(proportion = n/sum(n)) %>% 
  ggplot(aes(region, proportion,fill = region)) + 
  geom_bar(stat = "identity") + 
  facet_wrap(~ region, nrow = 2)

#Varios gráficos

x <- log10(murders$population)
y <- murders$total

p1 <- data.frame(x = x, y = y) %>% 
  ggplot(aes(x,y)) +
  geom_point()
p2 <- data.frame(x = x) %>%
  ggplot(aes(x = x)) +
  geom_histogram()

library(gridExtra)

## 
## Adjuntando el paquete: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

grid.arrange(p1, p2, ncol = 2)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.